The Sunday April 15, 2007 issue of the Houston Chronicle included a section devoted to real estate prices in Houston. In

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Please answer all parts in detail. I really need help,so any attempts to solve would be greatly appreciated (please donot copy from other posts).
Thank you.
The Sunday April 15, 2007 issue of the Houston Chronicle included a section devoted to real estate prices in Houston. In particular, data are presented on the 2006 median price per square foot for 1922 subdivisions. The data (HoustonRealEstate.txt) can be found on the book web site. Interest centers on developing a regression model to predict Y = 2006 median price per square foot from x₁ = %New Homes (i.e., of the houses that sold in 2006, the percentage that were - built in 2005 or 2006) x2₁ = %Foreclosures (i.e., of the houses that sold in 2006, the percentage that were identified as foreclosures) Table 4.3 Data on salaries Years of experience as a full professor 0 2 4 6 8 12 17 22 28 34 Sample size, n 17 33 19 25 18 60 58 31 34 19 Third quartile ($) 101,300 111,303 98,000 124,000 128,475 117,410 115,825 134,300 128,066 164,700
> Standardized Residuals 400 300 200 100 20 12 10 ∞0 6 + NON O 0.0 0.4 0.8 x1i do gegen Yi Square Root(Standardized Residuals!) 400 300- 200 100- 0.0 0.4 3.0- 2.0- 1.0- 0.0- 0 20 60 Fitted Values Figure 4.1 Plots associated with model (4.6) x2i 0 20 0.8 cias 60 Fitted Values x2i 1.0- 0.8- 0.6-80 0.4- 0.2- O 0.0- 0.0 0.4 x1i 0.8
for the i = 1, ... 1922 subdivisions. The first model considered was Y₁ = B₁ + B₁x₁₁ + B₂x₂; + e 0 Model (4.6) was fit used weighted least squares with weights, w₁ = n₁ where n₁ = the number of homes sold in subdivision i in 2006. Output from model (4.6), in the form of plots, appears in Figure 4.1. (4.6) (a) Explain it is necessary to use weighted least squares to fit model (4.6) and why w₁ = = n, is the appropriate choice for the weights. n₁ (b) Explain why (4.6) is not a valid regression model. (c) Describe what steps you would take to obtain a valid regression model (Figure 4.1).